Using Structural Connectivity to Reconstruct Brain Activation and Effective Connectivity - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Poster Année : 2020

Using Structural Connectivity to Reconstruct Brain Activation and Effective Connectivity

Résumé

Introduction: Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two noninvasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural informa on to do so. We present a source estimation algorithm that uses brain structural connectivity, obtained from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate auto-regressive model that explains the data in further time instants. This auto-regressive model can be thought as an estimation of the effective connectivity between brain regions. Methods: We use diffusion MRI (dMRI) in conjunction with EEG/MEG measurements to reconstruct brain activity. dMRI is processed using probabilistic fiber tracking from FSL. These fiber tracks are used in two ways: 1) to parcellate the cortex into functional regions (defined as having an homogeneous connectivity profile) and 2) to create a network of connexions between these regions. The parameters were tuned to obtain cortical regions of around 100 mm2, which is about the minimum size of detectable activations with M/EEG. We further assume a constant activation (a scalar value) per cortical region at a given time instant. This allows to greatly reduce the dimension of the source space from the ~10k nodes of the cortical mesh to ~600 extracted cortical regions. This spatial source model is completed by a temporal multivariate auto-regressive (MAR) model where a region activation at time t is obtained as linear combination of the activations at the p previous time instants t=1, ..., t-p of brain regions to which it is connected. The unknowns in this model are the region activations J(t) for the first t=1..p time instants and the coefficients A of the linear combination. Given some M/EEG measurements for a time window of size T, the goal is to estimate these unknowns to fit these data. To do so, we introduce a slightly modified MxNE criterion U(J) (Gramfort etal, 2012) that promotes spatial sparsity along with temporal continuity of the activations. A second criterion V(A) measures how well the estimated activations J(t), t=1..T are obeying the MAR model. The optimisa on procedure iterates between two steps, which alternatively improve the estimates of J(t), t=1..T to decrease U(J) and the linear coefficients A, given J(t), t=1..T. The process stops when a maximum number of iterations is reached or when there is no significant changes between two iterations. Results: The method was evaluated using the real dataset described in Wakeman et al (2015). MEG/EEG data were simultaneously recorded during a face recogni on task where a subject is shown famous, unknown or scrambled faces. The dataset also contains dMRI andT1 images. Four MAR models were tested:p∈ {1,2,3,4}. We show that the acquired data can be explained by our model with few regions as soon as p>1 (Fig. 1). Reconstructions using EEG and MEG show a clear negative peak at the FG around 200ms for p>1 which matches what can be found in the literature (Fig. 2). The non-null coefficient in the final A can be considered as the effective connectivity used during the task. Conclusions: We have presented a way of reconstructing brain activation and effective connectivity between the brain regions using an extension of the MxNE solver. Sources are constrained to follow a MAR model of order p. We have shown that such a model can fit real M/EEG measurements with relatively few activated regions as soon as p>1 and that the recovered activated regions are coherent with the task used to acquire the dataset.
Fichier principal
Vignette du fichier
poster.pdf (2.86 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02945690 , version 1 (22-09-2020)

Identifiants

  • HAL Id : hal-02945690 , version 1

Citer

Brahim Belaoucha, Théodore Papadopoulo. Using Structural Connectivity to Reconstruct Brain Activation and Effective Connectivity. Organization for Human Brain Mapping annual Meeting, Jun 2020, Montreal, Canada. , 2020. ⟨hal-02945690⟩
77 Consultations
48 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More